Skip Navigation


MBE Advance Access originally published online on April 20, 2009
Molecular Biology and Evolution 2009 26(8):1715-1721; doi:10.1093/molbev/msp080
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Supplementary Data
Right arrow All Versions of this Article:
26/8/1715    most recent
msp080v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Heger, A.
Right arrow Articles by Holmes, I.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Heger, A.
Right arrow Articles by Holmes, I.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2009. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Research Articles

Accurate Estimation of Gene Evolutionary Rates Using XRATE, with an Application to Transmembrane Proteins

Andreas Heger*, Chris P. Ponting* and Ian Holmes{dagger}

* Department of Physiology, Anatomy and Genetics, MRC Functional Genomics Unit, University of Oxford, Oxford, United Kingdom
{dagger} Department of Bioengineering, University of California

E-mail: andreas.heger{at}dpag.ox.ac.uk.

Accepted for publication April 11, 2009.

XRATE implements algorithms for comparative annotation, ancestral reconstruction, evolutionary rate estimation, and simulation. Its modeling repertoire includes phylogenetic stochastic context–free grammars and phylo-hidden Markov models. Following earlier tests of XRATE as a machine-learning tool suitable for alignment annotation, we now report the first tests of XRATE as a precise quantitative instrument for estimating evolutionary rates. We implement a codon model similar to that of Goldman and Yang (1994) (A codon-based model of nucleotide substitution for protein-coding DNA sequences. Mol Biol Evol 11: 725–736) and show that XRATE's parameter estimates are consistent with those of PAML. To demonstrate its utility, we apply the model to measure the difference in selective strength ({omega}) between intracellular and secreted regions of type I transmembrane proteins. In 215 of 303 instances, a complex model with individual {omega} for each region provides a better fit to the data than the simpler single {omega} value model. Secreted portions of type I transmembrane proteins show an elevation in {omega} similar to that seen for secreted protein genes. Less stringent purifying selection is thus a general property of the extracellular milieu, rather than being specific to only soluble and secreted proteins.

Key Words: evolutionary rates • expectation maximization • transmembrane proteins • stochastic context–free grammar

Jeffrey Thorne, Associate Editor


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.